Project Risk Lab

Based on the Oxford Global Projects database of thousands of projects worldwide:

You're deciding whether to proceed with a project. The decision hinges on an uncertain variable, such as expected revenue, NPV, or cost savings. If the true value falls below your threshold, proceeding would be a mistake. EVPI quantifies the maximum you should pay to resolve that uncertainty before committing.

The model’s key mechanism is the budget constraint acting as a selection filter. Organisations cannot fund every project, so they rank proposals by estimated benefit-cost ratio (BCR) and fund the top fraction (the budget share α). Among funded projects, costs systematically exceed estimates because the project was selected partly because noise made its costs look lower than they really were. This is the winner’s curse applied to project portfolios.

Two parameters govern the effect: σ (how noisy the estimates are) and α (how selective the budget is). Tighter budgets (lower α) force selection from the extreme tail of the estimated BCR distribution, amplifying the overrun. When α = 1 (everything funded), there is no selection and noise averages out.

When organisations rank projects by estimated benefit-cost ratio (BCR) and fund the top fraction α, projects with accidentally underestimated costs are more likely to be selected. The observed overrun in the reference class therefore reflects both genuine estimation bias and this statistical selection effect (a winner’s curse). But the new project being appraised will go through the same selection filter, so the selection-driven overrun will happen again regardless of any uplift. Applying the full observed overrun as an RCF adjustment therefore double-counts the selection component.

This tool decomposes the observed overrun into a selection component (teal) and a residual bias component (gold) using a closed-form expression for the expected cost ratio conditional on BCR-based selection, then constructs a corrected forecast curve that adjusts only for the bias portion.

All reference classes are conditional. When you pick "IT cost overruns" as your reference class, you've already conditioned on project type. The term Conditional Reference Class Forecasting adds a further condition: how far along the project is and what actual performance looks like so far. At 0% progress, you have only the unconditional distribution. At 50% progress with an observed cost ratio of 1.15, you combine that evidence with the reference class to produce a tighter, more informed forecast.

The parameters α (alpha) and β (beta) shape the distribution. Think of them as pseudo-counts: α = prior "hits" + 1 and β = prior "misses" + 1. Start with α=1, β=1 for a flat (uninformative) prior. Larger values indicate stronger prior belief, meaning a sharper peak and less spread. When you observe new data (hits and misses), the posterior is simply Beta(α + hits, β + misses).

Two forces compete: cost decline rewards waiting (technology gets cheaper), while the discount rate and finite benefit window penalize delay (future benefits are worth less, and the planning horizon shrinks). The model discounts all cash flows to year 0, so you can compare adopt-now vs. adopt-later on equal footing. The optimal year balances the savings from cheaper technology against the benefits forgone by waiting.

Risk matrices are ubiquitous in project management, but research shows they suffer from at least three fatal flaws: (1) verbal probability labels mean vastly different things to different people, (2) colour-coded cells hide enormous differences in expected loss, and (3) arbitrary category boundaries can reverse the ranking of risks. This interactive challenge lets you experience all three.

Five trades (Civil, Mechanical, E&I, Fit-out, Commissioning) must each process 35 rooms in sequence. Each trade rolls a die each week to determine capacity, but can only process rooms the previous trade has already completed. The deterministic plan, using the average roll of 3.5 rooms/week, expects completion in 14 weeks. You will play three rounds with dice of identical averages but different variability to see how uncertainty amplifies through the system.

This demonstrates a general principle: when tasks must happen in sequence and each step has variable output, the total project duration is always longer than the sum of averages. Planning with averages guarantees you will be late.

This data comes from a survey conducted by Wade Fagen-Ulmschneider (Teaching Professor, Computer Science, University of Illinois at Urbana-Champaign). Each of the 123 internet survey respondents was asked to assign a numeric probability (0–100%) to 17 common probability words. The box-and-whisker chart below shows the distribution of responses for each word: the box spans the interquartile range (Q1–Q3, the middle 50% of responses), the vertical line marks the median, and the whiskers extend to the minimum and maximum values observed. The study builds on a long tradition of research into verbal probability, notably Sherman Kent’s 1964 “Words of Estimative Probability” for the CIA.